The goals of the High Performance Biomedical Computing and Informatics Program are to identify and solve those complex biomedical problems that can benefit from high performance computing and communications hardware, scientific database and Web technologies, data mining and visualization techniques, and modern software engineering principles and efficient algorithms. This effort includes the followings: (1) developing high performance computational methods and algorithms to analyze biomedical data and to simulate complex biological systems, (2) developing knowledge-based data management systems for the discovery of biomedical knowledge, including medical image repository and clinical information management systems, (3) providing high performance computing resources and software tools to NIH researchers, including special-purpose parallel computing machines, and (4) collaborating with NIH researchers and colleagues at other research centers in applying information technology to biomedical research problems. Using high performance parallel computing and knowledge-based data management systems, biomedical scientists can greatly reduce the time it takes to complete computationally intensive tasks, adopt new approaches for processing experimental data, and mine large complex datasets to find important data patterns. These may allow for the inclusion of more data in a calculation, the determination of a more accurate result, a reduction in the time needed to complete a long calculation, and the implementation of a new algorithm or more realistic model. High performance parallel computing allows biomedical scientists to analyze and study large datasets that cannot be processed within a practical amount of computer analysis time on conventional sequential or vector processing computing machines. With high bandwidth network connections and interactive user interfaces, parallel computing is readily accessible to a biomedical researcher in the laboratory or clinic at the investigator's computer workstation. Knowledge-based databases and data mining tools are powerful resources in modern biomedical research. They provide researchers ways to extract and use information from vast amounts of knowledge and data collected from wide-ranging sources such as public and commercial databases (e.g., genome sequences, chromosome, SNP, genetic diseases, EST cluster, gene mapping, gene expression, molecular biology, and literature databases). In addressing these complex data analysis and management challenges, the High Performance Computing and Informatics Office (HPCIO) of the Division of Computational Bioscience (DCB) in the Center for Information Technology (CIT) is developing high performance computing hardware and software infrastructure for a wide range of biomedical applications where computational speed, advanced data analysis, smart data mining and large-scale data management are important. These include gene expression data analysis, image processing of live-cell arrays, medical image and clinical information management, biostatistics, population genetics, and human genetic linkage analysis. The ultimate goal is to provide high performance parallel computing, scientific databases, and data analysis and visualization tools to facilitate the science that is done at the NIH. While developing the computationally demanding applications and scientific database systems, HPCIO is investigating the following challenging issues: (1) partitioning a problem into many parts that can be independently executed on different processors; (2) designing the parts so that the computing load can be distributed evenly over the available processors or dynamically balanced; (3) designing algorithms so that the number of processors is a parameter and the algorithms can be configured dynamically for the available machine; (4) developing tools and environments for producing portable parallel programs; (5) incorporating iinteractive data analysis and visualization tools into the user environment; (6) monitoring system performance; (7) proving that a parallel algorithm on a given machine meets its specifications; (8) evaluating modern parallel computer architectures for their performance characteristics on biomedical applications; and (9) developing and providing high performance database systems and data mining tools for archive and analysis of scientific data and medical images via Web-interfaces.